

import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp


def     __SCREAMING_SNAKE_CASE   ():
						snake_case_  =      ArgumentParser(
						    description=(
						        '''PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'''
						    )			)

						# Optional arguments for the launch helper
						parser.add_argument('''--num_cores'''    ,						type=SCREAMING_SNAKE_CASE__    ,						default=1    ,						help='''Number of TPU cores to use (1 or 8).'''			)

						# positional
						parser.add_argument(
						    '''training_script'''    ,						type=SCREAMING_SNAKE_CASE__    ,						help=(
						        '''The full path to the single TPU training '''
						        '''program/script to be launched in parallel, '''
						        '''followed by all the arguments for the '''
						        '''training script'''
						    )    ,						)

						# rest from the training program
						parser.add_argument('''training_script_args'''    ,						nargs=SCREAMING_SNAKE_CASE__			)

						return parser.parse_args()


def     __SCREAMING_SNAKE_CASE   ():
						snake_case_  =      parse_args()

						# Import training_script as a module.
						snake_case_  =      Path(args.training_script			)
						sys.path.append(str(script_fpath.parent.resolve()			)			)
						snake_case_  =      script_fpath.stem
						snake_case_  =      importlib.import_module(SCREAMING_SNAKE_CASE__			)

						# Patch sys.argv
						snake_case_  =      [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores			)]

						xmp.spawn(mod._mp_fn    ,						args=()    ,						nprocs=args.num_cores			)


if __name__ == "__main__":
					main()







'''simple docstring'''





import argparse
import collections
import json
import os
import re
import string
import sys

import numpy as np


_UpperCamelCase						:						List[str]  =					re.compile(R'\b(a|an|the)\b', re.UNICODE)

_UpperCamelCase						:						Optional[int]  =					None
def __UpperCAmelCase   (					)    ->   Optional[Any]:
			UpperCAmelCase_      :       List[Any]          =       argparse.ArgumentParser('''Official evaluation script for SQuAD version 2.0.'''		)
			parser.add_argument('''data_file'''			,       metavar='''data.json'''			,       help='''Input data JSON file.'''		)
			parser.add_argument('''pred_file'''			,       metavar='''pred.json'''			,       help='''Model predictions.'''		)
			parser.add_argument(
			    '''--out-file'''			,       '''-o'''			,       metavar='''eval.json'''			,       help='''Write accuracy metrics to file (default is stdout).'''		)
			parser.add_argument(
			    '''--na-prob-file'''			,       '''-n'''			,       metavar='''na_prob.json'''			,       help='''Model estimates of probability of no answer.'''		)
			parser.add_argument(
			    '''--na-prob-thresh'''			,       '''-t'''			,       type=A			,       default=1.0			,       help='''Predict "" if no-answer probability exceeds this (default = 1.0).'''			,       )
			parser.add_argument(
			    '''--out-image-dir'''			,       '''-p'''			,       metavar='''out_images'''			,       default=A			,       help='''Save precision-recall curves to directory.'''		)
			parser.add_argument('''--verbose'''			,       '''-v'''			,       action='''store_true'''		)
			if len(sys.argv		) == 1:
						parser.print_help()
						sys.exit(1		)
			return parser.parse_args()
def __UpperCAmelCase   (					A					:			int		)    ->   Optional[Any]:
			UpperCAmelCase_      :       Optional[Any]          =       {}
			for article in dataset:
						for p in article["paragraphs"]:
									for qa in p["qas"]:
												UpperCAmelCase_      :       Union[str, Any]          =       bool(qa['''answers''']['''text''']		)
			return qid_to_has_ans
def __UpperCAmelCase   (					A					:			List[Any]		)    ->   Dict:

			def remove_articles(A					:			Dict		):
						return ARTICLES_REGEX.sub(''' '''			,       A		)

			def white_space_fix(A					:			str		):
						return " ".join(text.split()		)

			def remove_punc(A					:			Tuple		):
						UpperCAmelCase_      :       Dict          =       set(string.punctuation		)
						return "".join(ch for ch in text if ch not in exclude		)

			def lower(A					:			Any		):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(A		)		)		)		)
def __UpperCAmelCase   (					A					:			Optional[Any]		)    ->   Tuple:
			if not s:
						return []
			return normalize_answer(A		).split()
def __UpperCAmelCase   (					A					:			Optional[int]			,       A					:			Dict		)    ->   List[str]:
			return int(normalize_answer(A		) == normalize_answer(A		)		)
def __UpperCAmelCase   (					A					:			Optional[Any]			,       A					:			List[Any]		)    ->   str:
			UpperCAmelCase_      :       Tuple          =       get_tokens(A		)
			UpperCAmelCase_      :       Union[str, Any]          =       get_tokens(A		)
			UpperCAmelCase_      :       Dict          =       collections.Counter(A		) & collections.Counter(A		)
			UpperCAmelCase_      :       Optional[int]          =       sum(common.values()		)
			if len(A		) == 0 or len(A		) == 0:
						# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
						return int(gold_toks == pred_toks		)
			if num_same == 0:
						return 0
			UpperCAmelCase_      :       int          =       1.0 * num_same / len(A		)
			UpperCAmelCase_      :       Any          =       1.0 * num_same / len(A		)
			UpperCAmelCase_      :       Any          =       (2 * precision * recall) / (precision + recall)
			return fa
def __UpperCAmelCase   (					A					:			Union[str, Any]			,       A					:			str		)    ->   Union[str, Any]:
			UpperCAmelCase_      :       List[Any]          =       {}
			UpperCAmelCase_      :       str          =       {}
			for article in dataset:
						for p in article["paragraphs"]:
									for qa in p["qas"]:
												UpperCAmelCase_      :       Optional[Any]          =       qa['''id''']
												UpperCAmelCase_      :       List[str]          =       [t for t in qa['''answers''']['''text'''] if normalize_answer(A		)]
												if not gold_answers:
															# For unanswerable questions, only correct answer is empty string
															UpperCAmelCase_      :       Tuple          =       ['''''']
												if qid not in preds:
															print(F"Missing prediction for {qid}"		)
															continue
												UpperCAmelCase_      :       Tuple          =       preds[qid]
												# Take max over all gold answers
												UpperCAmelCase_      :       List[Any]          =       max(compute_exact(A			,       A		) for a in gold_answers		)
												UpperCAmelCase_      :       int          =       max(compute_fa(A			,       A		) for a in gold_answers		)
			return exact_scores, fa_scores
def __UpperCAmelCase   (					A					:			Optional[int]			,       A					:			Tuple			,       A					:			Optional[Any]			,       A					:			List[Any]		)    ->   Optional[int]:
			UpperCAmelCase_      :       Tuple          =       {}
			for qid, s in scores.items():
						UpperCAmelCase_      :       Union[str, Any]          =       na_probs[qid] > na_prob_thresh
						if pred_na:
									UpperCAmelCase_      :       List[str]          =       float(not qid_to_has_ans[qid]		)
						else:
									UpperCAmelCase_      :       Any          =       s
			return new_scores
def __UpperCAmelCase   (					A					:			Any			,       A					:			Any			,       A					:			str=None		)    ->   int:
			if not qid_list:
						UpperCAmelCase_      :       str          =       len(A		)
						return collections.OrderedDict(
						    [
						        ('''exact''', 100.0 * sum(exact_scores.values()		) / total),
						        ('''f1''', 100.0 * sum(fa_scores.values()		) / total),
						        ('''total''', total),
						    ]		)
			else:
						UpperCAmelCase_      :       Dict          =       len(A		)
						return collections.OrderedDict(
						    [
						        ('''exact''', 100.0 * sum(exact_scores[k] for k in qid_list		) / total),
						        ('''f1''', 100.0 * sum(fa_scores[k] for k in qid_list		) / total),
						        ('''total''', total),
						    ]		)
def __UpperCAmelCase   (					A					:			Union[str, Any]			,       A					:			Dict			,       A					:			Union[str, Any]		)    ->   Dict:
			for k in new_eval:
						UpperCAmelCase_      :       str          =       new_eval[k]
def __UpperCAmelCase   (					A					:			str			,       A					:			Tuple			,       A					:			Any			,       A					:			List[str]		)    ->   Optional[int]:
			plt.step(A			,       A			,       color='''b'''			,       alpha=0.2			,       where='''post'''		)
			plt.fill_between(A			,       A			,       step='''post'''			,       alpha=0.2			,       color='''b'''		)
			plt.xlabel('''Recall'''		)
			plt.ylabel('''Precision'''		)
			plt.xlim([0.0, 1.05]		)
			plt.ylim([0.0, 1.05]		)
			plt.title(A		)
			plt.savefig(A		)
			plt.clf()
def __UpperCAmelCase   (					A					:			Optional[Any]			,       A					:			Optional[Any]			,       A					:			Optional[int]			,       A					:			Optional[int]			,       A					:			str=None			,       A					:			Any=None		)    ->   Any:
			UpperCAmelCase_      :       Optional[Any]          =       sorted(A			,       key=lambda A		: na_probs[k]		)
			UpperCAmelCase_      :       int          =       0.0
			UpperCAmelCase_      :       List[Any]          =       1.0
			UpperCAmelCase_      :       Optional[int]          =       0.0
			UpperCAmelCase_      :       Optional[Any]          =       [1.0]
			UpperCAmelCase_      :       List[Any]          =       [0.0]
			UpperCAmelCase_      :       Dict          =       0.0
			for i, qid in enumerate(A		):
						if qid_to_has_ans[qid]:
									true_pos += scores[qid]
						UpperCAmelCase_      :       List[Any]          =       true_pos / float(i + 1		)
						UpperCAmelCase_      :       Union[str, Any]          =       true_pos / float(A		)
						if i == len(A		) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
									# i.e., if we can put a threshold after this point
									avg_prec += cur_p * (cur_r - recalls[-1])
									precisions.append(A		)
									recalls.append(A		)
			if out_image:
						plot_pr_curve(A			,       A			,       A			,       A		)
			return {"ap": 100.0 * avg_prec}
def __UpperCAmelCase   (					A					:			List[Any]			,       A					:			str			,       A					:			Any			,       A					:			Optional[int]			,       A					:			Any			,       A					:			List[str]		)    ->   List[Any]:
			if out_image_dir and not os.path.exists(A		):
						os.makedirs(A		)
			UpperCAmelCase_      :       Any          =       sum(1 for v in qid_to_has_ans.values() if v		)
			if num_true_pos == 0:
						return
			UpperCAmelCase_      :       Union[str, Any]          =       make_precision_recall_eval(
			    A			,       A			,       A			,       A			,       out_image=os.path.join(A			,       '''pr_exact.png'''		)			,       title='''Precision-Recall curve for Exact Match score'''			,       )
			UpperCAmelCase_      :       List[str]          =       make_precision_recall_eval(
			    A			,       A			,       A			,       A			,       out_image=os.path.join(A			,       '''pr_f1.png'''		)			,       title='''Precision-Recall curve for F1 score'''			,       )
			UpperCAmelCase_      :       Dict          =       {k: float(A		) for k, v in qid_to_has_ans.items()}
			UpperCAmelCase_      :       Optional[int]          =       make_precision_recall_eval(
			    A			,       A			,       A			,       A			,       out_image=os.path.join(A			,       '''pr_oracle.png'''		)			,       title='''Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)'''			,       )
			merge_eval(A			,       A			,       '''pr_exact'''		)
			merge_eval(A			,       A			,       '''pr_f1'''		)
			merge_eval(A			,       A			,       '''pr_oracle'''		)
def __UpperCAmelCase   (					A					:			List[str]			,       A					:			Tuple			,       A					:			str			,       A					:			List[Any]		)    ->   Dict:
			if not qid_list:
						return
			UpperCAmelCase_      :       List[Any]          =       [na_probs[k] for k in qid_list]
			UpperCAmelCase_      :       Union[str, Any]          =       np.ones_like(A		) / float(len(A		)		)
			plt.hist(A			,       weights=A			,       bins=2_0			,       range=(0.0, 1.0)		)
			plt.xlabel('''Model probability of no-answer'''		)
			plt.ylabel('''Proportion of dataset'''		)
			plt.title(F"Histogram of no-answer probability: {name}"		)
			plt.savefig(os.path.join(A			,       F"na_prob_hist_{name}.png"		)		)
			plt.clf()
def __UpperCAmelCase   (					A					:			str			,       A					:			Dict			,       A					:			List[str]			,       A					:			int		)    ->   Optional[Any]:
			UpperCAmelCase_      :       Tuple          =       sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]		)
			UpperCAmelCase_      :       Optional[Any]          =       num_no_ans
			UpperCAmelCase_      :       int          =       cur_score
			UpperCAmelCase_      :       List[Any]          =       0.0
			UpperCAmelCase_      :       Dict          =       sorted(A			,       key=lambda A		: na_probs[k]		)
			for i, qid in enumerate(A		):
						if qid not in scores:
									continue
						if qid_to_has_ans[qid]:
									UpperCAmelCase_      :       Tuple          =       scores[qid]
						else:
									if preds[qid]:
												UpperCAmelCase_      :       Optional[int]          =       -1
									else:
												UpperCAmelCase_      :       int          =       0
						cur_score += diff
						if cur_score > best_score:
									UpperCAmelCase_      :       Dict          =       cur_score
									UpperCAmelCase_      :       Dict          =       na_probs[qid]
			return 100.0 * best_score / len(A		), best_thresh
def __UpperCAmelCase   (					A					:			int			,       A					:			List[Any]			,       A					:			Union[str, Any]			,       A					:			Tuple			,       A					:			str			,       A					:			Union[str, Any]		)    ->   Tuple:
			UpperCAmelCase_    ,      UpperCAmelCase_      :       List[Any]          =       find_best_thresh(A			,       A			,       A			,       A		)
			UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[int]          =       find_best_thresh(A			,       A			,       A			,       A		)
			UpperCAmelCase_      :       List[str]          =       best_exact
			UpperCAmelCase_      :       List[Any]          =       exact_thresh
			UpperCAmelCase_      :       Any          =       best_fa
			UpperCAmelCase_      :       str          =       fa_thresh
def __UpperCAmelCase   (					)    ->   Tuple:
			with open(OPTS.data_file		) as f:
						UpperCAmelCase_      :       Optional[int]          =       json.load(A		)
						UpperCAmelCase_      :       str          =       dataset_json['''data''']
			with open(OPTS.pred_file		) as f:
						UpperCAmelCase_      :       List[Any]          =       json.load(A		)
			if OPTS.na_prob_file:
						with open(OPTS.na_prob_file		) as f:
									UpperCAmelCase_      :       List[str]          =       json.load(A		)
			else:
						UpperCAmelCase_      :       List[str]          =       {k: 0.0 for k in preds}
			UpperCAmelCase_      :       Optional[Any]          =       make_qid_to_has_ans(A		)  # maps qid to True/False
			UpperCAmelCase_      :       Optional[int]          =       [k for k, v in qid_to_has_ans.items() if v]
			UpperCAmelCase_      :       Dict          =       [k for k, v in qid_to_has_ans.items() if not v]
			UpperCAmelCase_    ,      UpperCAmelCase_      :       List[Any]          =       get_raw_scores(A			,       A		)
			UpperCAmelCase_      :       Optional[int]          =       apply_no_ans_threshold(A			,       A			,       A			,       OPTS.na_prob_thresh		)
			UpperCAmelCase_      :       Tuple          =       apply_no_ans_threshold(A			,       A			,       A			,       OPTS.na_prob_thresh		)
			UpperCAmelCase_      :       int          =       make_eval_dict(A			,       A		)
			if has_ans_qids:
						UpperCAmelCase_      :       Optional[int]          =       make_eval_dict(A			,       A			,       qid_list=A		)
						merge_eval(A			,       A			,       '''HasAns'''		)
			if no_ans_qids:
						UpperCAmelCase_      :       Dict          =       make_eval_dict(A			,       A			,       qid_list=A		)
						merge_eval(A			,       A			,       '''NoAns'''		)
			if OPTS.na_prob_file:
						find_all_best_thresh(A			,       A			,       A			,       A			,       A			,       A		)
			if OPTS.na_prob_file and OPTS.out_image_dir:
						run_precision_recall_analysis(A			,       A			,       A			,       A			,       A			,       OPTS.out_image_dir		)
						histogram_na_prob(A			,       A			,       OPTS.out_image_dir			,       '''hasAns'''		)
						histogram_na_prob(A			,       A			,       OPTS.out_image_dir			,       '''noAns'''		)
			if OPTS.out_file:
						with open(OPTS.out_file			,       '''w'''		) as f:
									json.dump(A			,       A		)
			else:
						print(json.dumps(A			,       indent=2		)		)


if __name__ == "__main__":
						_UpperCamelCase						:						Union[str, Any]  =					parse_args()
						if OPTS.out_image_dir:
												import matplotlib

												matplotlib.use('Agg')
												import matplotlib.pyplot as plt
						main()


